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The Pittsboro Case Study
The third objective of this project is to test the chosen statistical classification method for
the case study town of Pittsboro. Both standard HHC input data and CLUSTER data
based on GIS property tax data are used in the four- step travel demand model for
Pittsboro to test the results of the traditional HHC method to the CLUSTER method. The
outputs of the trip generation step are compared using a t- test. Assuming the zonal
productions from the two different methods are considered a paired sample, the difference
between trips produced by each zone is calculated. The resultant differences for each zone
become a single sample of differences about which inferences can be made. The null
hypothesis is that there is no difference between trips resulting from the HHC or
CLUSTER input data. Therefore, the mean of the sample of differences is compared to an
expected mean ( m D) of zero using a one sample t- test. The test demonstrates that the
productions and attractions produced by the two methods do not compare well for the two
models at a 95% confidence level. However, the mean difference between productions for
the HBW and NHB trip purposes are quite low. The mean difference for the HBW is 3.69
productions per TAZ between the two models and 2.76 for the NHB productions. In
practical application of the trip generation model these differences are negligible. The
same trend is documented for the attractions
Since the most important validation of a model compares traffic ground counts to
estimated traffic, a comparison of flows versus ground counts is also undertaken for both
methods. A comparison of the pre- calibration HHC and the CLUSTER models shows a
mean percent difference between ground counts and link assignments greater than 25%
which is well above the acceptable limits for calibrated NCDOT models. Mean percent
difference between ground count and flows for the HHC model is greater than that found
using the CLUSTER model. The CLUSTER model also results in a slightly better ground
count to flow ratio than does the HHC model. Both models have the same 26 links with
flow rate error within acceptable ranges. These results indicate that the pre- calibration
flows derived using the CLUSTER method are no less accurate than those obtained using
the HHC model. Statistical differences between CLUSTER model flows and ground
counts are likely an issue that can be dealt with in the calibration phase of modeling. If
the HHC model can be calibrated then the CLUSTER model should also be able to be
calibrated and percent differences brought within acceptable limits. This indicates that
CLUSTER model data, based on GIS property tax information, is no less accurate
an input to IDS than is the windshield survey data.
The benefit of using the CLUSTER model is the timesaving associated with its use. The
windshield survey of Pittsboro took 104 person- hours to complete the 100% evaluation of
households. Obtaining the GIS data from Chatham County required no more than a 10-
minute telephone conversation but did require some data cleansing efforts before
applying the NISS clustering method. Data cleansing involves reducing the complete
parcel level data down to a data set that only includes single family dwelling units with
parcel identification number, deed acre, improvement value and land value attributes. The
NISS clustering model is not very straightforward and requires significant statistical
knowledge to be able to apply it to a GIS property tax data set. Total classification with
the CLUSTER method, including data cleansing, would require 8- 16 person hours ( once
ES-
4

The Pittsboro Case Study
The third objective of this project is to test the chosen statistical classification method for
the case study town of Pittsboro. Both standard HHC input data and CLUSTER data
based on GIS property tax data are used in the four- step travel demand model for
Pittsboro to test the results of the traditional HHC method to the CLUSTER method. The
outputs of the trip generation step are compared using a t- test. Assuming the zonal
productions from the two different methods are considered a paired sample, the difference
between trips produced by each zone is calculated. The resultant differences for each zone
become a single sample of differences about which inferences can be made. The null
hypothesis is that there is no difference between trips resulting from the HHC or
CLUSTER input data. Therefore, the mean of the sample of differences is compared to an
expected mean ( m D) of zero using a one sample t- test. The test demonstrates that the
productions and attractions produced by the two methods do not compare well for the two
models at a 95% confidence level. However, the mean difference between productions for
the HBW and NHB trip purposes are quite low. The mean difference for the HBW is 3.69
productions per TAZ between the two models and 2.76 for the NHB productions. In
practical application of the trip generation model these differences are negligible. The
same trend is documented for the attractions
Since the most important validation of a model compares traffic ground counts to
estimated traffic, a comparison of flows versus ground counts is also undertaken for both
methods. A comparison of the pre- calibration HHC and the CLUSTER models shows a
mean percent difference between ground counts and link assignments greater than 25%
which is well above the acceptable limits for calibrated NCDOT models. Mean percent
difference between ground count and flows for the HHC model is greater than that found
using the CLUSTER model. The CLUSTER model also results in a slightly better ground
count to flow ratio than does the HHC model. Both models have the same 26 links with
flow rate error within acceptable ranges. These results indicate that the pre- calibration
flows derived using the CLUSTER method are no less accurate than those obtained using
the HHC model. Statistical differences between CLUSTER model flows and ground
counts are likely an issue that can be dealt with in the calibration phase of modeling. If
the HHC model can be calibrated then the CLUSTER model should also be able to be
calibrated and percent differences brought within acceptable limits. This indicates that
CLUSTER model data, based on GIS property tax information, is no less accurate
an input to IDS than is the windshield survey data.
The benefit of using the CLUSTER model is the timesaving associated with its use. The
windshield survey of Pittsboro took 104 person- hours to complete the 100% evaluation of
households. Obtaining the GIS data from Chatham County required no more than a 10-
minute telephone conversation but did require some data cleansing efforts before
applying the NISS clustering method. Data cleansing involves reducing the complete
parcel level data down to a data set that only includes single family dwelling units with
parcel identification number, deed acre, improvement value and land value attributes. The
NISS clustering model is not very straightforward and requires significant statistical
knowledge to be able to apply it to a GIS property tax data set. Total classification with
the CLUSTER method, including data cleansing, would require 8- 16 person hours ( once
ES-
4